From Digital Design to Laminated Module: An End-to-End Workflow for Efficiency and Colour Prediction of Ceramically Printed Photovoltaics

Digital ceramic printing enables visually versatile, coloured building-integrated photovoltaics (BIPV) for architecturally sensitive contexts. However, printed frontglass patterns attenuate incident light and introduce local shading, increasing hotspot risk and efficiency losses. Designers currently lack integrated methods to evaluate these trade-offs prior to module fabrication. To bridge this gap, we present an end-to-end workflow that predicts the relative efficiency (RE), layer composition, and effective colour of printed PV modules directly from printer-ready RIP files. A physically motivated multilayer printing simulation determines the ink layer surface coverage and resulting RE. Preliminary tests show low bias, with simulated RE deviations within ca. ±5% (mean 0.28%, σ = 3.26%). Inverting the simulated RE through multivariate regression yields the layer compositions for a target RE. The effective colour is predicted in L*a*b* colourspace by a trained neural network with a mean CIE difference ΔE2000 of 1.86 (σ = 0.92). Effective colours may be grouped into equificiency classes with an RE difference ΔRE of 5–10% to support flexible design exploration within target efficiency margins and hotspot mitigation. Three case studies demonstrate the workflow’s applicability across educational, design, and built domains, including a full-scale façade installation.